1. Technical Field
This invention relates to diagnostic ultrasonic imaging in general and to ultrasonic imaging of the elastic properties of scanned tissue in particular.
2. Description of the Related Art
Tissue elastic properties convey important diagnostic information. Consequently, palpationxe2x80x94the pressing of tissue to feel for differences in elasticityxe2x80x94has been used since ancient times as a simple but effective diagnostic technique. Even to this day, for example, most breast cancers are discovered by self-examination using manual palpation, and physicians still rely on palpation to detect potential tumors of the liver and prostate.
The principle of manual palpation is accordingly well known and is based on the property that if a compressive force is applied to an elastic body, then it will deform. If a relatively stiffer, that is, less deformable, inclusion is located within a region of the body, then a constant compressive displacement will deform the region above the stiff object more than the adjacent regions. Because tissues are elastic, the more they are deformed, the greater counter force they generate; in other words, large stress leads to large deformation. If a diagnostician applies the pressure with her fingers, then she will often be able to feel the stress distribution above the palpated region. To sum up the procedure, if one presses on body tissue, then one can often feel xe2x80x9clumps.xe2x80x9d
One property of tissue elasticity that is not as intuitive is that the distribution of stress that results from a compressive force applied to the top of the region under investigation is not uniform throughout the region. Rather, the stress difference that is large close to the target decays rapidly further from the target. In other words, differences in tissue stiffness are not as noticeable when the stiffer object is deeper within the body. Stress decay therefore limits the depth at which one can palpate tumors manuallyxe2x80x94if the tumor is too deep, then one cannot feel it at all.
Ultrasonic elasticity imaging is a technique that emulates palpation. According to this technique, an ultrasound transducer is used as a remote sensing device to scan an object within an interrogation region of the body both before and after a compression is applied. The 2-D displacement function is then estimated by comparing the pre- and post-compression scans. Object strain and/or elastic constants can then be estimated from the estimated displacement function.
Ultrasonic elasticity imaging has several advantages over palpation. One advantage is that it can provide information about tissue elasticity as deep as the ultrasound can penetrate, whereas manual palpation senses stress only near the surface. Another advantage is that ultrasonic elasticity imaging has relatively high sensitivity, although resolution and sensitivity can be reduced for deeper inclusions. Ultrasonic elasticity imaging can also provide a 2-D cross sectional view of the elastic properties of the object that are within the sound beam. For example, using axial strain images, one can often detect malignant lesions and estimate their location and geometry. One other obvious advantage of ultrasonic elasticity imaging is that the image acquisition process is non-invasive and poses no risk to patients.
With these advantages, ultrasonic elasticity imaging has found many applications such as tumor detection, assessment of early renal disease, and vascular disease diagnosis. Moreover, because the elastic properties of tissue play an important role in tissue characterization, many more clinical applications can be found once image quality can be reliably maintained. Examples of known applications of ultrasonic elasticity imaging are disclosed in:
M. Bilgen and M. F. Insana, xe2x80x9cDeformation models and correlation analysis in 9 elastography,xe2x80x9d J. Acoust. Soc. Am. 99(5): 3212-3224, 1996;
I. Cespedes, J. Ophir, H. Ponnekanti, and N. Maklad, xe2x80x9cElastography: Elasticity imaging using ultrasound with application to muscle and breast in vivo,xe2x80x9d Ultrason. Imaging 15: 73-88, 1993;
E. J. Chen, R. S. Adler, P. L. Carson, W. K. Jenkins, and W. D. O""Brien, xe2x80x9cUltrasound tissue displacement imaging with application to breast cancer,xe2x80x9d Ultrason. Med. Biol. 21(9): 1153-1162, 1995;
B. S. Garra, E. I. Cespedes, J. Ophir, S. R. Spratt, R. A. Zuurbier, C. M. Magnant, and M. F. Pennanen, xe2x80x9cElastography of breast lesions: initial clinical results,xe2x80x9d Radiology 202(1):, 79-86, 1997;
T. J. Hall, P. Chaturvedi, M. F. Insana, J. G. Wood, H. Khant, and Y. Zhu, xe2x80x9cTracking progressive renal disease with quantitative ultrasonic imaging,xe2x80x9d IEEE xe2x80x9cUltrasonics Symposium Proc. 98CH36102: 1769-1772, 1998;
S. H. Huang, xe2x80x9cPrinciples of sonoelasticity imaging and its applications in hard tumor detection,xe2x80x9d Ph.D. thesis, University of Rochester, Rochester, N.Y., 1990;
T. A. Krouskop, D. R. Dougherty, and S. F. Vinson, xe2x80x9cA pulsed Doppler ultrasonic system for making non-invasive measurements of the mechanical properties of soft tissues,xe2x80x9d J. Rehab Res Dev 24(2):1-8, 1987;
M. Krueger, A. Pesavento, H. Ermert, K. M. Hiltawsky, L. Heuser, H. Rosenthal, and A. Jensen, xe2x80x9cUltrasonic strain imaging of the female breast using phase root seeking and three-dimensional xe2x80x98optical flowxe2x80x99,xe2x80x9d IEEE Ultrasonics Symposium Proc. 98CH36102: 1757-1760, 1998;
L. Gao, K. J. Parker, R. M. Lerner, and S. F. Levinson, xe2x80x9cImaging of the elastic properties of the tissue: A review,xe2x80x9d Ultrason. Med. Biol. 22(8): 959-977, 1996;
K. Motoi, H. Morita, N. Fujita, Y. Takano, K. Muzushige, S. Senda, and S H. Matsuo, xe2x80x9cStiffness of human arterial wall assessed by intravascular ultrasound,xe2x80x9d J. Cardio. 25: 189-197, 1995;
J. Ophir, E. I. Cespedes, H. Ponnekanti, Y. Yazdi, and x. Li, xe2x80x9cElastography: a quantitative method for imaging the elasticity of biological tissues,xe2x80x9d Ultrasonic Imaging 13: 111-134, 1991;
A. P. Sarvazyan, A. R. Skovoroda, S. Y. Emelianov, J. B. Fowikes, J. G. Pipe, R. S. Adler, R. B. Buxton, and P. L. Carson, xe2x80x9cBiophysical bases of elasticity imaging,xe2x80x9d Acoust. Imaging 21:223-240, 995; and
M. Tristam, D. C. Barbosa, D. O. Cosgrove, D. K. Nassiri, J. C. Bamber, and C. R. Hill, xe2x80x9cUltrasonic study of in vivo kinetic characteristics of human tissues,xe2x80x9d Ultrason. Med. Biol. 12(12): 927-937, 1986.
There are, accordingly, several displacement/strain estimation methods in the prior art. The algorithms underlying these known methods typically rely on cross-correlation, echo data modeling, block matching, direct strain estimation using adaptive local stretching algorithm, and the analysis of a deformable model. These known methods are outlined here.
Cross-correlation techniques have been widely used in sonar and radar systems since their inception in the 1940""s. In sonar, marine vessels tow an array of acoustic sensors. A passive, 1-D sonar array then listens for externally generated sound while an active sonar system transmits sound pulses and listens for corresponding echoes. The time delays between signals received by different sensors in the array are then computed using cross-correlation. The relative distance and bearing of the echo source can then be computed from the estimated time delay.
Cross-correlation has also been applied to the problem of estimating the elastic properties of biological tissue. Published International Patent Application PCT/EP99/03769, xe2x80x9cSystem for Rapidly Calculating Expansion Images from High-Frequency Ultrasonic Echo Signals,xe2x80x9d Pesa Vento and Helmut Ermert, published Dec. 2, 1999. The displacement in time between at least two different echo signals is determined by iteratively evaluating the phases of a plurality of the complex values of the cross-correlation function. In order to achieve the desired speed, this method restricts evaluation to echo signals from points on the same A-line.
The general time delay estimation problem can be stated more rigorously as follows: A signal, s(t), which is generated by a remote source, is detected by two sensors. Because the distances between the sensors and the source are different, the detected signals from these sensors can be written as:
s1(t)=s(t)+n1(t)
s2(t)=s(t+D)+n2(t)
where D is the time delay; and n1(t) and n2(t) are noise processes that are independent of s(t). In this data model, one assumes that s(t) is a deterministic signal, that the speed of sound c is constant, and that the delay D is independent of time. The time delay estimation problem thus involves estimating D from the observed signals s1(t) and s2(t). The details of different time-delay estimation methods can be found in:
E. K. Al-Hussaini and S. A. Kassam, xe2x80x9cRobust Eckart filters for time delay estimation,xe2x80x9d IEEE Trans. Acoust., Speech, Signal Processing 32(5): 1052-1063, 1984;
R. Cusani, xe2x80x9cFast techniques for time delay estimation,xe2x80x9d Proc. MELECON ""89 177-180, 1989; and
C. H. Knapp and G. C. Carter, xe2x80x9cThe generalized correlation method for estimation of time delay,xe2x80x9d IEEE Trans. Acoust., Speech, Signal Processing 24(4): 320-327, 1976.
The performance of time-delay estimation techniques is analyzed in, for example:
E. Weinstein and A. Weiss, xe2x80x9cFundamental limitations in passive time delay estimation-part I: narrow-band systems,xe2x80x9d IEEE Trans. Acoust., Speech, Signal Processing 31(2): 472-485, 1983; and
xe2x80x9cFundamental limitations in passive time delay estimation-part II: wide-band systems,xe2x80x9d IEEE Trans. Acoust., Speech, Signal Processing 32(5): 1064-1077, 1984.
Medical ultrasonic elasticity imaging is analogous to time-delay estimation as described above, although s(t) is usually stochastic and D is often a function of time. When it is appropriate to assume a constant sound speed, time delay will still be directly proportional to displacement. On the other hand, stochastic signals and time-dependent delays greatly increase the complexity of displacement estimation in medical ultrasonic elasticity imaging as compared with the simpler time-delay problem found in sonar.
The echo data model for a deformed biological medium can be summarized as follows: Assume that tissue-scattering sources located within the interrogation region of the body may be described by a 3-D function z(x), where x is a point in 3-D space. The pre- and post-compression echo signals can then be written as:
r1(x)=[h{circumflex over (x)}z](x)+m(x)
r2(x)=[h{circumflex over (x)}z](x+xcex94(x)+n2(x))
where h is a function that describes the imaging system, xcex94(x) is a function that describes the tissue deformation, and h and z are convolved. The goal is then to estimate the function xcex94(x) from the observed signals r1 and r2.
The complexity of this method for soft-tissue displacement estimation stems from two aspects that can be inferred from the equations. First, if the deformation applied to the tissue is on the order of or larger than the resolution of the imaging system, represented by h, then r1 and r2 will decorrelate and it will be impossible to track object motion. This model for the echo signals is shown in, for example, R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, xe2x80x9cStatistics of speckle in ultrasound B-scans,xe2x80x9d IEEE Trans Sonics and Ultrasonics 30(3): 156-163, 1983. In other words, the echo waveforms found in r1 are not always present in r2 so that echo signals are not conserved under deformation. If this happens, then displacement cannot be estimated. Second, unlike in sonar applications, where D is independent of time, the displacement of a deformed tissue in sonography can be an arbitrary function of time. As will become clear from the discussion below, general displacement in medical ultrasonography is a problem in which thousands of variables must be estimated.
In the early works of soft tissue displacement estimation, tissue displacement is approximated by piece-wise constants: Each A-line is divided into equal-length segments and a single delay value is estimated for each segment. Examples of the use of this method are found in:
R. J. Dickinson and C. R. Hill, xe2x80x9cMeasurement of soft tissue motion using correlation between A scans,xe2x80x9d Ultrasound Med. Biol. 8(3): 263-271, 1982;
D. Dotti, E. Gatti, V. Svelto, A. Ugge, and P. Vidali, xe2x80x9cBlood flow measurements by ultrasound correlation techniques,xe2x80x9d Energia Nucleare 23: 571-575, 1976;
P. G. M. De Jong, T. Arts, and A. P. G. Hoeks, xe2x80x9cDetermination of tissue motion velocity by correlation interpolation of pulsed ultrasonic echo signals,xe2x80x9d Ultrasonic Imaging 12: 84-98, 1990;
M. O""Donnell, A. R. Skovoroda, and B. M. Shapo, xe2x80x9cMeasurement of arterial wall motion using Fourier based speckle tracking algorithms,xe2x80x9d IEEE Ultrasonic Symposium Proc. 1101-1104, 1991;
M. O""Donnell, A. R. Skovoroda, B. M. Shapo, and S. Emelianov, xe2x80x9cInternal displacement and strain imaging using ultrasonic speckle tracking,xe2x80x9d IEEE Trans. Ultrason. Ferro Freq. Contr. 41(3): 314-325, 1994;
J. Ophir, E. I. Cespedes, H. Ponnekanti, Y. Yazdi, and x. Li, xe2x80x9cElastography: a quantitative method for imaging the elasticity of biological tissues,xe2x80x9d Ultrasonic imaging 13: 111-134, 1991;
G. E. Trahey, S. M. Hubbard, and O. T. Von Ramm, xe2x80x9cAngle independent blood flow detection by frame-to-frame correlation of B-mode images,xe2x80x9d Ultrasonics 26: 271-276, 1988;
L. S. Wilson and D. E. Robinson, xe2x80x9cUltrasonic measurement of small displacements and deformations of tissues,xe2x80x9d Ultrasonic Imaging 4: 71-82, 1982; and
S. Yagi and K. Nakayama, xe2x80x9cLocal displacement analysis of inhomogeneous soft tissue by spatial correlation of rf echo signals,xe2x80x9d Proceedings 1988 World Federation for Ultrasound in Medicine and Biology, 133, 1988.
This approach is possible because the deformation of biological tissues is a relatively smooth function of position. The performance of this has been evaluated in, for example:
M. Bilgen and M. F. Insana, xe2x80x9cDeformation models and correlation analysis in elastography,xe2x80x9d (referenced above);
xe2x80x9cError analysis in acoustic elastography: I. displacement estimation,xe2x80x9d J. Acoust. Soc. Am. 101(2): 1139-1146, 1997;
xe2x80x9cError analysis in acoustic elastography: II. strain estimation and SNR analysis,xe2x80x9d J. Acoust. Soc. Am. 101(2): 1147-1154, 1997; and
E. I. Cespedes, M. F. Insana, and J. Ophir, xe2x80x9cTheoretical bounds on strain estimation in elastography,xe2x80x9d IEEE Trans. Ultrason. Ferro Freq. Contr. 42(5): 969-972, 1995.
Due to the physical compression applied to the tissue, a somewhat more realistic approximation to the displacement function can be described as a piece-wise linear function:
xcex94(x)=xcex5ix+Di, xxcex5(xixe2x88x921,xi)
where xcex5 is a scaling factor (or strain). Analytical and simulation studies have shown, however, that for xcex5 greater than 0.05, even the improved piece-wise conventional cross-correlation method does not accurately estimate the displacement function. One proposal for overcoming this limitation involves a method known as xe2x80x9cglobal temporal stretching.xe2x80x9dThis technique is described in:
xe2x80x9cReduction of signal decorrelation from mechanical compression of tissues by temporal stretching: Application to elastography,xe2x80x9d Ultrason. Med. Biol. 23(1): 95-105, 1997; and
E. I. Cespedes and J. Ophir, xe2x80x9cReduction of image noise in elastography,xe2x80x9d Ultrasonic Imaging 15: 89-102, 1993.
This technique requires some a priori knowledge of the applied compression. With this knowledge, one can then globally stretch r2 the same amount as the applied compression. In other words, one geometrically transforms r2(x) above to obtain a stretched post-compression rf echo function, where the transformation is such that the x1 component of r2 is scaled by the factor (1xe2x88x92xcex5), where xcex5 is the applied total strain.
Global stretching alleviates the problem caused by cross-correlating strained signals by reducing the amount of the strain between r1 and r2 and thus increasing coherence. In the regions where the local strain variation is large, however, the residual strain after stretching can still remain large. Large residual strain leads in turn to large displacement errors and ultimately to high noise values in strain images. Although efforts have been made to average the estimated strain over multiple compression scans to reduce the noise (see, for example, T. Varghese, J. Ophir, and I. Cespedes, xe2x80x9cNoise reduction in elastograms using temporal stretching with multicompression averaging,xe2x80x9d Ultrasound in Med. Biol. 22(8): 1043-1052, 1996), decorrelation errors are a fundamental problem in strain imaging. This is because of a failure of the forward model for deformation on which image formation algorithms are based. Hence, averaging cannot solve the problem completely.
Two other methods for solving this problem are popular: The first approach is to use a block-matching algorithm to perform a low-resolution displacement estimation after global stretching is applied. This method is described in, for example,
P. Chaturvedi, M. F. Insana, and T. J. Hall, xe2x80x9c2-D companding for noise reduction in strain imaging,xe2x80x9d IEEE Trans. Ultrason. Ferroelect. Freq. Contr. 45(1): 179-191, 1998;
xe2x80x9cTesting the limitations of 2-D local companding in strain imaging using phantoms,xe2x80x9d IEEE Trans. Ultrason. Ferroelect. Freq. Contr. 45(4): 179-191, 1998; and
M. F. Insana, P. Chaturvedi, T. J. Hall, and M. Bilgen, xe2x80x9c3-D companding using linear arrays for improved strain imaging,xe2x80x9d IEEE Ultrasonics Symposium Proc., 97CH36118: 1435-1438, 1997.
According to this method, after the global stretching, the post-compression scan is warped according to the block matching results. With this processing, the strain between pre- and warped post-compression scans is greatly reduced. Cross-correlation is then applied to perform the final displacement estimation. Results of simulations and experiments have shown this to be a more effective technique than others in the prior art, although the results depend on the boundary conditions and the applied deformation.
One improvement in the warping process can be achieved by applying what is known as a xe2x80x9cdeformable mesh.xe2x80x9d This algorithm, and analytical support for it, are described in:
M. F. Insana, L. T. Cook, M. Bilgen, P. Chaturvedi, and Y. Zhu, xe2x80x9cMaximum-likelihood approach to strain imaging using ultrasound,xe2x80x9d J. Acoust. Soc. Am. 107(3), 1421-1434, 2000;
Y. Zhu, P. Chaturvedi, and M. F. Insana, xe2x80x9cStrain imaging with a deformable mesh,xe2x80x9d Ultrasonic imaging 21: 127-146, 1999; and
Y. Zhu, M. F. Insana, P. Chaturvedi, T. J. Hall, H. Khant, L. T. Cook, and J. M. Gauch, xe2x80x9cDeformable mesh algorithm for strain imaging with complex tissue deformation,xe2x80x9d IEEE Ultrasonics Symposium Proc. 98CH36102: 1769-1772, 1998.
Yet another known approach is to estimate strain directly by an adaptive local stretching algorithm, in which each segment of the echo signals in the post-compression is xe2x80x9cstretchedxe2x80x9d repeatedly in order to find a best match with a segment in the pre-compression echo signals. The estimated stretching factor is then the estimated strain. A problem with this method is that the comparison segment must be large (2 mm) in order to lower the estimation noise; this can result in distortion in the strain images when the object strain varies significantly in the stretch window. This technique is described in:
S. K. Alam and J. Ophir, xe2x80x9cReduction of signal decorrelation for mechanical compression of tissues by temporal stretching: applications to elastography,xe2x80x9d (referenced above); and
S. K. Alam, J. Ophir, and E. E. Konofagou, xe2x80x9cAn adaptive strain estimator for elastography,xe2x80x9d IEEE Trans. Ultrason. Ferroelect. Freq. Contr. 45(2): 461-472, 1998.
The standard block matching algorithm, which can be shown to be a variant of a 2-D cross correlation algorithm, is widely used in digital video compression techniques to estimate the motion vectors of rectangular blocks. See H. Gharavi and M. Mills, xe2x80x9cBlock-matching motion estimation algorithms: new results,xe2x80x9d IEEE Trans. Circ. and Syst. 37: 649-651, 1990; and J. R. Jain and A. K. Jain, xe2x80x9cDisplacement measurement and its application in interframe image coding,xe2x80x9d IEEE Trans. Commun. 29: 1799-1808, 1981.
A hierarchical block matching algorithm has also been developed in order to decrease the computational load of this method; the algorithm has also been adapted for use in tracking tissue motion. See, for example:
M. Bierling, xe2x80x9cDisplacement estimation by hierarchical block-matching,xe2x80x9d Proc. Visual Comm. and Image Proc., SPIE 1001: 942-951, 1988;
A. M. Tekalp, xe2x80x9cDigital video processing,xe2x80x9d Prentice Hall, Upper Saddle River, N.J., 1995; and
F. Yeung, S. F. Levinson, and K. J. Parker, xe2x80x9cMultilevel and motion model-based ultrasonic speckle tracking algorithms,xe2x80x9d Ultrasound in Med. and Biol. 24(3): 427-441,
In order to detect and track edges in an image, an active contour or xe2x80x9csnakexe2x80x9d model was proposed by M. Kass, A. Witkin, and D. Terzopoulos in xe2x80x9cSnakes: active contour models,xe2x80x9d International Journal for Computer Vision 1: 321-331, 1988, which later evolved into a well known general-technique known as the xe2x80x9cdeformable model.xe2x80x9d The deformable model can be used not only to detect 3-D surfaces and track their deformation from a sequence of images, but it can also be used to synthesize complex shapes. This technique is described in:
W. C. Huang and D. B. Goldgof, xe2x80x9cAdaptive-size meshes for rigid and non-rigid shape analysis and synthesis,xe2x80x9d IEEE Transactions on Pattern Analysis and Machine Intelligence 15(6): 611-616, 1993;
xe2x80x9cPoint correspondence recovery in non-rigid motion using nonlinear finite element modeling,xe2x80x9d Proceedings of Asian Conference on Computer Vision: 256-259, 1993;
A. Pentland and B. Horowitz, xe2x80x9cRecovery of non-rigid motion and structure,xe2x80x9d IEEE transactions on pattern analysis and machine intelligence 13(7): 730-742, 1991.
D. Terzopoulos and K. Fleischer, xe2x80x9cDeformable models,xe2x80x9d The visual computer 4: 306-331, 1988; and
D. Terzopoulos, A. Witkin, and M. Kass, xe2x80x9cConstraints on deformable models: recovering 3D shape and nonrigid motion,xe2x80x9d Artificial intelligence 36: 91-123, 1988.
Motivated by the ideas behind the deformable model, xe2x80x9cdeformable meshxe2x80x9d algorithms have been developed to estimate non-rigid motions from 2-D images. See, for example,
J. Inagawa and T. Maejima, xe2x80x9cNon-rigid motion tracking of image sequences based on smoothness constraints,xe2x80x9d Systems and Computers in Japan 26(2): 45-53, 1995;
Y. Wang and O. Lee, xe2x80x9cActive meshxe2x80x94a feature seeking and tracking image sequence representation scheme,xe2x80x9d IEEE Trans. Image Process. 3: 610-624, 1994; and
xe2x80x9cUse of two-dimensional deformable mesh structures for video coding,xe2x80x9d IEEE Trans. Circuits and Systems Video Techno. 6: 636-659, 1996.
According to this method, the 2-D data space is partitioned by a mesh, which is composed of contiguous and non-overlapping polygons called xe2x80x9cpatches.xe2x80x9d The vertices of the polygons are called xe2x80x9cnodes,xe2x80x9d which are used as control points. By interpolating displacements of the nodes, these control points uniquely determine the displacement function over the meshed region. Adjusting nodal displacements, the deformable mesh algorithm tries to match two images by geometrically transforming one of them according to nodal displacements.
By transmitting displacement information only about the nodal points, the deformable mesh algorithm may also be used to compress image data and minimize data transmission. To do so, however, nodal locations must be chosen such that the interpolated images best resemble the original images in a least-squares sense. An energy function is then derived, which measures the matching error between two image frames (for displacement estimation) and reconstruction error (for image reconstruction of the first frame). By minimizing the energy function, these systems obtain displacement estimates and optimal nodal locations at the same time.
As is described by F. Yeung, S. F. Levinson, D. Fu, and K. J. Parker, in xe2x80x9cFeature-adaptive motion tracking of ultrasound image sequences using a deformable mesh,xe2x80x9d IEEE Trans. Medical Imaging 17(6): 945-956, 1998; in xe2x80x9cThe use of an adaptive mesh for feature tracking in sonoelasticity,xe2x80x9d Ultrasonic Imaging 20 (1998, abstract), 61; and in the two papers by Y. Zhu referenced above, the deformable mesh algorithm has also been adapted to ultrasonic tissue displacement estimation. In F. Yeung, et al""s implementation, B-scans are used as the input data. The unique feature of this implementation is that the original patches are further divided into sub-patches, based on the band-pass energy and the image interpolation error; it is unclear, however, how the band-pass energy and interpolation errors affect the accuracy of displacement estimation.
Current data acquisition systems reported in the literature for elasticity imaging are cumbersome and in most cases inadequate for clinical imaging. Most of these systems are adapted from laboratory data acquisition systems. They generally use motor-driven compression which can provide smooth displacement, but they fail to provide real-time feedback of the tissue strain. Real-time strain image feedback is essential to determine the quality of the acquired data. B-mode image data shows gross tissue motion, but is inadequate to determine when, for example, elevational motion is too large to allow motion tracking. Descriptions of current data acquisition systems for elasticity imaging are provided in:
S. Y. Emelianov, M. A. Lubinski, A. R. Skovoroda, et al., xe2x80x9cReconstructive ultrasound elasticity imaging for renal transplant diagnosis: Kidney ex vivo results,xe2x80x9d Ultrasonic Imaging 22: 178-194, 2000;
E. I. Cespedes, C. L. de Korte, A. F. W. van der Steen, xe2x80x9cEcho decorrelation from displacement gradients in elasticity and velocity estimation,xe2x80x9d IEEE Trans Ultrason, Ferroelec, Freq Control 46(4): 791-801, 1999;
B. S. Garra, E. I. Cespedes, J Ophir, et al., xe2x80x9cElastography of breast lesions: Initial clinical results,xe2x80x9d (referenced above);
xe2x80x9cTesting the limits of 2-D companding for strain imaging using phantomsxe2x80x9d (referenced above), and;
A. Lorenz, H-J. Sommerfeld, M. Garcia-Schurmann, et al., xe2x80x9cA new system for the acquisition of ultrasonic multicompression strain images of the human prostate in vivo,xe2x80x9d IEEE Trans Ultrason, Ferroelec, Freq Control 46(5): 1147-1154, 1999.
In addition, numerous devices are described in the 2000 IEEE Ultrasonics Symposium Proceedings vol. 00CH37121: 1767-1868.
What is needed is a method and a corresponding system for estimating tissue elasticity using ultrasound that has an improved contrast-to-noise ratio as compared with the prior art, that is able to handle large deformations, and that imposes a computational burden low enough to enable at least substantially real-time imaging of the elasticity of tissue within a 2-D imaging region. This invention provides such a method and system.
The invention provides an ultrasound imaging method and related system implementation according to which a region of interest (ROI) of a patient""s body is repeatedly scanned with an ultrasound transducer. Varying stress is then applied to the ROI. First and second ultrasound echo samples are then acquired at first and second stress levels, respectively, with the second stress level differing from the first and with the first and second echo samples each representing the ROI. A reference sample set is then selected among the first ultrasound echo samples. For each of the first samples in the reference sample set, the system searches within a first search region for a corresponding second sample. For selected ones of the first echo samples not in the reference sample set, the system also searches within a second search region for the corresponding second sample. The second search region is smaller than the first search region, measured in number of samples included in the respective search region. The system then estimates displacement of tissue within the ROI from the difference in estimated positions between the first and second samples and therefrom generates and displays a first representation of a function (such as strain) of the estimated tissue displacement.
In the preferred embodiment of the invention, for each sample in the reference sample set, a displacement vector estimate is calculated that has first and second displacement components that correspond to estimated displacement of the respective sample in a first and a second direction, respectively. For at least selected ones of a plurality of non-reference samples, which are selected from the first sample set, the system assigns, as the second displacement component, the calculated second displacement component of the sample in the reference sample set with which the respective non-reference sample is collocated in the second direction. The first displacement component is then calculated corresponding to estimated displacement in the first direction.
The first search region is preferably two-dimensional, with the second search region being one-dimensional. The first direction preferably extends axially from the transducer and the second direction is preferably a lateral direction that is perpendicular to the first direction.
One advantage of the invention is that it enables real-time imaging of displacement, or a function of displacement such as strain. Accordingly, in a real-time embodiment of the invention, the first representation is generated and updated with a frame rate that is fast enough to allow for coordination by a user of transducer movement with the display of the first representation. The invention allows for a frame rate of at least one frame per second; indeed, a rate of several frames per second is achievable.
The invention also includes a dual-mode processing embodiment. In this embodiment, the first representation is generated with a first quality in a real-time mode. In a post-processing, non-real time mode, however, the first and second ultrasound echo data samples are compared and a post-processed displacement estimate of the tissue within the ROI is calculated, after which a second representation of the post-processed displacement estimate is generated with a second quality that is greater than the first quality.
The search region is preferably adjustable based on different criteria, both spatial and temporal, or both. For example, tissue displacement may be estimated as a displacement vector at each of a plurality of reference depths. For each successive reference depth, a measure of reliability of the estimated displacement vector is then calculated and the search region used to compute a subsequent displacement vector estimate is adjusted based on the reliability of the displacement vector estimate for the corresponding sample located at the previous reference depth.
One method for temporal adjustment according to the invention involves storing the first representation of the function of the estimated tissue displacement for a plurality of image frames and then calculating a frame-to-frame displacement difference value for each of a plurality of corresponding samples. The display of each sample having a displacement difference value greater than a predetermined maximum value is then adjusted accordingly, for example, by temporally smoothing the difference values greater than the predetermined maximum.
As another aspect of the preferred embodiment of the invention, the second search region is dynamically adjusted. In this embodiment, the dimensions sup, sdown, sleft, and sright of the search region are adjusted for samples in a neighborhood of a reference base sample as a function of the estimated reliability, where sup and sdown are numbers of samples above and below the base sample, respectively, in an axial direction, and sleft and sright samples on either side of the base sample in a lateral direction. The parameters sup and sdown are then preferably set to a predetermined function of (xcex5max1* xcex41) and sleft and sright are set to a predetermined function of (xcex5max2* xcex42). Here, xcex5max1 and xcex5max2 are maximum possible strains in the axial and lateral directions, respectively; and xcex41, and xcex42 are distances in the axial and lateral directions, respectively, between the base sample and an assumed optimum second sample. The first samples located in a shallowest portion of the region of interest are preferably used as the reference sample set where the shallowest portion has a depth of DROI, measured in the axial direction, and a width WROI, measured in the lateral direction. For current base samples within the shallowest portion, the first search region is then preferably selected to have the dimensions sup, sdown, sleft, and sright, measured in samples. The second search region is then preferably a sample window centered on the second sample displaced from the current base sample by the amount of the previously estimated displacement of the second sample directly above it in the axial direction.
If needed, a smoothed displacement vector is calculated for selected ones of the first samples in the reference sample set. The displacement vector estimate is then compared with the smoothed displacement vector. If the absolute difference between any component of the displacement vector estimate exceeds a corresponding component of the smoothed displacement vector by a predetermined threshold, the system then sets that individual component of the displacement vector estimate equal to the corresponding component of the smoothed displacement vector. The smoothed displacement vector may be calculated using curve fitting, based, for example, in regression, of the components of the a combined set of individual displacement vectors for the first samples in the reference sample set.